Digital images and video frames can usually include many undividable spatial elements arranged in rectangle shapes. These elements can be called “pixels”. In today's typical applications, a number of the pixels in the images and video frames can range from tens of thousands to tens of millions. Each of the pixels can include one or more values. For example, each of the pixels can include one value in greyscale images, three values in color images, and dozens of values in hyperspectral images.
Spatial domain representation of the images and video frames can generally record pixel values in raster order including left-to-right then top-to-bottom. Two popular spatial domain representations can include bitmap (BMP) images and uncompressed audio-video-interleave (AVI) videos. The spatial domain representation can retain all details of the images and video frames but can demand large data sizes.
Alternatively, the images and video frames can be represented as a linear combination of special functions. The functions can usually be perpendicular to each other and can be called “basis functions”. Instead of recording the pixel values, coefficients of the basis functions can be recorded to represent the images and video frames. Such representation can be called “transformation domain representation” of the images and video frames.
A process to obtain the transformation domain representation of the images and video frames can be called “image transform”. Popular image transforms can include, but not limited to, Fourier transforms, local cosine transforms, and wavelet transforms.
The Fourier transforms can represent the images and video frames as linear combinations of sine and cosine functions. The local cosine transforms can partition the images and video frames into blocks of certain sizes, and the pixel values can become linear combinations of cosine functions inside each block. The wavelet transforms can represent the images and video frames as linear combinations of wavelet basis functions.
The transformation domain representations of the image and video frames can generally be “sparse”. In other words, many coefficients of the basis functions can be either zero or very close to zero. The “sparse” property of the transformation domain representation can be widely used in many important image and video applications, such as compression, denoising, demosaicing, etc.
In image and video compression, in order to reduce data storage and transmission requirements, certain image data can be selectively discarded to reduce an amount of data needed to represent the image while avoiding substantial degradation of an appearance of the image. The image and video compressions can generally be conducted on transformation domains. Transform coefficients can be suppressed to zero or individually quantized to reduce an amount of data that is needed to represent the image.
Image compression methods can be widely implemented in capture devices, such as cameras, camcorders, smart phones, and tablets, as examples. If the image transforms can be applied at an encoder side of the image and video compressions, residue artifacts can often be observed after decompression at a decoder side, which is a main drawback of the image and video compressions. Such residue artifacts can generally resemble the basis functions and can be more visible when bitrates are low or file sizes are small. To ensure satisfying images or videos, it is an important task to suppress such residue artifacts on the decoder side.
The digital images and video frames can often be corrupted by various noises, such as shot noise, thermal noise, quantization noise, etc. Image denoising is a process to suppress these noises and improve image and video quality. Most of modern color imaging sensors can only capture one color per pixel location and can rely on nearby pixels' information to recover values of the other two colors. A process to recover full resolution color information can be called “demosaicing”. Denoising and demosaicing can be two essential steps in most of today's digital cameras including, but not limited to, point-and-shoot cameras, single-lens-reflex (SLR) cameras, camcorders, mobile phone cameras, and tablet cameras.
Transformation domain methods, especially wavelet transforms, can often be applied in image denoising and demosaicing tasks to achieve better image and video qualities. One particular downside of wavelet-based denoising and demosaicing can relate to the residue artifacts. Therefore, it is an essential step to suppress such residue artifacts.
Thus, a need still remains for an image processing system that can deliver good picture quality and features across a wide range of device with different sizes, resolutions, and connectivity. In view of the increasing demand for providing video on the growing spectrum of intelligent devices, it is increasingly critical that answers be found to these problems. In view of the ever-increasing commercial competitive pressures, along with growing consumer expectations and the diminishing opportunities for meaningful product differentiation in the marketplace, it is critical that answers be found for these problems. Additionally, the need to reduce costs, improve efficiencies and performance, and meet competitive pressures adds an even greater urgency to the critical necessity for finding answers to these problems.
Solutions to these problems have been long sought but prior developments have not taught or suggested any solutions and, thus, solutions to these problems have long eluded those skilled in the art.